SOTAVerified

Common Sense Reasoning

Common sense reasoning tasks are intended to require the model to go beyond pattern recognition. Instead, the model should use "common sense" or world knowledge to make inferences.

Papers

Showing 501550 of 939 papers

TitleStatusHype
COMMA-DEER: COmmon-sense Aware Multimodal Multitask Approach for Detection of Emotion and Emotional Reasoning in Conversations0
KC-ISA: An Implicit Sentiment Analysis Model Combining Knowledge Enhancement and Context FeaturesCode0
Do ever larger octopi still amplify reporting biases? Evidence from judgments of typical colour0
Decentralized Vehicle Coordination: The Berkeley DeepDrive Drone Dataset and Consensus-Based Models0
ERNIE-mmLayout: Multi-grained MultiModal Transformer for Document Understanding0
Assessment of cognitive characteristics in intelligent systems and predictive ability0
The Embeddings World and Artificial General Intelligence0
Elaboration-Generating Commonsense Question Answering at ScaleCode0
JARVIS: A Neuro-Symbolic Commonsense Reasoning Framework for Conversational Embodied Agents0
On Reality and the Limits of Language Data: Aligning LLMs with Human Norms0
Exploiting Sentiment and Common Sense for Zero-shot Stance DetectionCode0
Intrinsically Motivated Learning of Causal World Models0
Neuro-Symbolic Learning: Principles and Applications in Ophthalmology0
PASTA: A Dataset for Modeling Participant States in Narratives0
WinoGAViL: Gamified Association Benchmark to Challenge Vision-and-Language ModelsCode0
V-Coder: Adaptive AutoEncoder for Semantic Disclosure in Knowledge Graphs0
Reasoning about Actions over Visual and Linguistic Modalities: A Survey0
Ask Me What You Need: Product Retrieval using Knowledge from GPT-30
A Systematic Survey of Text Worlds as Embodied Natural Language Environments0
Is “My Favorite New Movie” My Favorite Movie? Probing the Understanding of Recursive Noun Phrases0
0/1 Deep Neural Networks via Block Coordinate Descent0
Symbolic image detection using scene and knowledge graphsCode0
Extracting Zero-shot Common Sense from Large Language Models for Robot 3D Scene Understanding0
RELATE: Generating a linguistically inspired Knowledge Graph for fine-grained emotion classification0
Towards the Detection of a Semantic Gap in the Chain of Commonsense Knowledge Triples0
An Informational Space Based Semantic Analysis for Scientific Texts0
Leveraging QA Datasets to Improve Generative Data AugmentationCode0
A Survey on Semantics in Automated Data Science0
Identifying relevant common sense information in knowledge graphsCode0
Detecting COVID-19 Conspiracy Theories with Transformers and TF-IDF0
Irony Detection for Dutch: a Venture into the Implicit0
Trans-KBLSTM: An External Knowledge Enhanced Transformer BiLSTM Model for Tabular Reasoning0
On the Limitations of Dataset Balancing: The Lost Battle Against Spurious Correlations0
A very preliminary analysis of DALL-E 20
Deep Unsupervised Hashing with Latent Semantic Components0
K-VQG: Knowledge-aware Visual Question Generation for Common-sense Acquisition0
Efficient Language Modeling with Sparse all-MLP0
Embarrassingly Simple Performance Prediction for Abductive Natural Language InferenceCode0
Integration of knowledge and data in machine learning0
Russian SuperGLUE 1.1: Revising the Lessons not Learned by Russian NLP models0
Neural NID Rules0
NEWSKVQA: Knowledge-Aware News Video Question Answering0
An Application of Pseudo-Log-Likelihoods to Natural Language Scoring0
Evaluating Machine Common Sense via Cloze Testing0
COPA-SSE: Semi-structured Explanations for Commonsense ReasoningCode0
Combining Fast and Slow Thinking for Human-like and Efficient Navigation in Constrained Environments0
On the Limitations of Dataset Balancing: The Lost Battle Against Spurious Correlations0
Unsupervised Common Sense Relation Extraction0
CommonsenseQA 2.0: Exposing the Limits of AI through Gamification0
Towards Automated Error Analysis: Learning to Characterize Errors0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ST-MoE-32B 269B (fine-tuned)Accuracy96.1Unverified
2Unicorn 11B (fine-tuned)Accuracy91.3Unverified
3CompassMTL 567M with TailorAccuracy90.5Unverified
4CompassMTL 567MAccuracy89.6Unverified
5UnifiedQA 11B (fine-tuned)Accuracy89.4Unverified
6Claude 3 Opus (5-shot)Accuracy88.5Unverified
7GPT-4 (5-shot)Accuracy87.5Unverified
8ExDeBERTa 567MAccuracy87Unverified
9LLaMA-2 13B + MixLoRAAccuracy86.3Unverified
10LLaMA3 8B+MoSLoRAAccuracy85.8Unverified
#ModelMetricClaimedVerifiedStatus
1GPT-4 (few-shot, k=25)Accuracy96.4Unverified
2PaLM 2 (few-shot, CoT, SC)Accuracy95.1Unverified
3Shivaay (4B, few-shot, k=8)Accuracy91.04Unverified
4StupidLLMAccuracy91.03Unverified
5Claude 2 (few-shot, k=5)Accuracy91Unverified
6Claude 1.3 (few-shot, k=5)Accuracy90Unverified
7PaLM 540B (Self Improvement, Self Consistency)Accuracy89.8Unverified
8PaLM 540B (Self Consistency)Accuracy88.7Unverified
9PaLM 540B (Self Improvement, CoT Prompting)Accuracy88.3Unverified
10PaLM 540B (Self Improvement, Standard-Prompting)Accuracy87.2Unverified
#ModelMetricClaimedVerifiedStatus
1ST-MoE-32B 269B (fine-tuned)Accuracy95.2Unverified
2LLaMA 3 8B+MoSLoRA (fine-tuned)Accuracy90.5Unverified
3PaLM 2-L (1-shot)Accuracy89.7Unverified
4PaLM 2-M (1-shot)Accuracy88Unverified
5LLaMA-3 8B + MixLoRAAccuracy86.5Unverified
6Camelidae-8×34BAccuracy86.2Unverified
7PaLM 2-S (1-shot)Accuracy85.6Unverified
8LLaMA 65B + CFG (0-shot)Accuracy84.2Unverified
9GAL 120B (0-shot)Accuracy83.8Unverified
10LLaMA-2 13B + MixLoRAAccuracy83.5Unverified
#ModelMetricClaimedVerifiedStatus
1Turing NLR v5 XXL 5.4B (fine-tuned)EM95.9Unverified
2ST-MoE-32B 269B (fine-tuned)EM95.1Unverified
3T5-11BF194.1Unverified
4DeBERTa-1.5BEM94.1Unverified
5PaLM 540B (finetuned)EM94Unverified
6Vega v2 6B (fine-tuned)EM93.9Unverified
7PaLM 2-L (one-shot)F193.8Unverified
8T5-XXL 11B (fine-tuned)EM93.4Unverified
9PaLM 2-M (one-shot)F192.4Unverified
10PaLM 2-S (one-shot)F192.1Unverified